from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
import torch
from PIL import Image, ImageDraw, ImageFont
from modelscope import dataset_snapshot_download, snapshot_download
import random


def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False):
    # Create a blank image for overlays
    overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))

    colors = [
        (165, 238, 173, 80),
        (76, 102, 221, 80),
        (221, 160, 77, 80),
        (204, 93, 71, 80),
        (145, 187, 149, 80),
        (134, 141, 172, 80),
        (157, 137, 109, 80),
        (153, 104, 95, 80),
        (165, 238, 173, 80),
        (76, 102, 221, 80),
        (221, 160, 77, 80),
        (204, 93, 71, 80),
        (145, 187, 149, 80),
        (134, 141, 172, 80),
        (157, 137, 109, 80),
        (153, 104, 95, 80),
    ]
    # Generate random colors for each mask
    if use_random_colors:
        colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]

    # Font settings
    try:
        font = ImageFont.truetype("wqy-zenhei.ttc", font_size)  # Adjust as needed
    except IOError:
        font = ImageFont.load_default(font_size)

    # Overlay each mask onto the overlay image
    for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
        # Convert mask to RGBA mode
        mask_rgba = mask.convert('RGBA')
        mask_data = mask_rgba.getdata()
        new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
        mask_rgba.putdata(new_data)

        # Draw the mask prompt text on the mask
        draw = ImageDraw.Draw(mask_rgba)
        mask_bbox = mask.getbbox()  # Get the bounding box of the mask
        text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10)  # Adjust text position based on mask position
        draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)

        # Alpha composite the overlay with this mask
        overlay = Image.alpha_composite(overlay, mask_rgba)

    # Composite the overlay onto the original image
    result = Image.alpha_composite(image.convert('RGBA'), overlay)

    # Save or display the resulting image
    result.save(output_path)

    return result

def example(pipe, seeds, example_id, global_prompt, entity_prompts):
    dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png")
    masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))]
    negative_prompt = ""
    for seed in seeds:
        # generate image
        image = pipe(
            prompt=global_prompt,
            cfg_scale=4.0,
            negative_prompt=negative_prompt,
            num_inference_steps=30,
            seed=seed,
            height=1024,
            width=1024,
            eligen_entity_prompts=entity_prompts,
            eligen_entity_masks=masks,
        )
        image.save(f"eligen_example_{example_id}_{seed}.png")
        visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png")


vram_config = {
    "offload_dtype": "disk",
    "offload_device": "disk",
    "onload_dtype": torch.float8_e4m3fn,
    "onload_device": "cpu",
    "preparing_dtype": torch.float8_e4m3fn,
    "preparing_device": "cuda",
    "computation_dtype": torch.bfloat16,
    "computation_device": "cuda",
}
pipe = QwenImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
    ],
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)
snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen", allow_file_pattern="model.safetensors")
pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen/model.safetensors", hotload=True)

# example 1
global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n"
entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"]
example(pipe, [0], 1, global_prompt, entity_prompts)

# example 2
global_prompt = "samurai girl wearing a kimono, she's holding a sword  glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render."
entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "yellow belt"]
example(pipe, [0], 2, global_prompt, entity_prompts)
